Fam Zheng
d923aa1e31
Hopfield + Hebbian hybrid memory system for LLMs. Two nights of experiments (16 iterations), validated on LongMemEval (ICLR 2025). Architecture: - Single-hop: Two-Stage Hopfield (NN top-20 → softmax settle) - Multi-hop: Hebbian W matrix with WTA pattern separation - 64% on LongMemEval (500 questions), retrieval-only, no LLM dependency - 4ms latency @ 20K memories, ~1GB VRAM Key findings: - Hopfield attention solved noise tolerance (20% → 100% vs flat Hebbian) - WTA pattern separation enables 20K+ capacity - Multi-hop associative chains (6 hops, CosSim=1.0) — RAG can't do this - MiniLM-L6 is optimal (discrimination gap > absolute similarity) - Paraphrase cue augmentation: 55% → 100% on synthetic, 36% → 64% on benchmark - SNN encoder viable (CosSim 0.99) but not needed for current architecture
256 lines
8.5 KiB
Python
256 lines
8.5 KiB
Python
"""Experiment: LongMemEval benchmark on HippocampalMemory.
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Protocol:
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1. For each question, load all haystack sessions as conversation history
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2. Extract memories from each session turn (user says X, assistant says Y)
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3. Store in HippocampalMemory with paraphrase augmentation
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4. Query with the question
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5. Check if the recalled memories contain the answer
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This tests our system against a real, published benchmark.
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"""
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import sys
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import json
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import time
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from pathlib import Path
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from collections import Counter
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import torch
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import torch.nn as nn
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from nuonuo.hippocampus import HippocampalMemory
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from llm import generate_paraphrases_heuristic
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DEVICE = "cuda"
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def load_model():
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from sentence_transformers import SentenceTransformer
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return SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
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def emb(model, text):
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return model.encode([text], convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE)[0]
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def emb_batch(model, texts):
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if not texts:
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return []
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embs = model.encode(texts, convert_to_tensor=True,
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normalize_embeddings=True, device=DEVICE,
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batch_size=64)
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return [embs[i] for i in range(embs.shape[0])]
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def extract_memories_from_session(session):
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"""Extract (cue, target) pairs from a conversation session.
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Strategy: pair consecutive user/assistant turns.
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User message = cue, assistant response = target (truncated to key info).
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"""
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memories = []
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for i, turn in enumerate(session):
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if turn["role"] == "user":
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user_text = turn["content"].strip()
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# Find next assistant response
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for j in range(i + 1, len(session)):
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if session[j]["role"] == "assistant":
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assistant_text = session[j]["content"].strip()
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# Truncate long responses to first 200 chars
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if len(assistant_text) > 200:
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# Try to cut at sentence boundary
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cut = assistant_text[:200].rfind(". ")
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if cut > 50:
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assistant_text = assistant_text[:cut + 1]
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else:
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assistant_text = assistant_text[:200]
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if len(user_text) > 10 and len(assistant_text) > 10:
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memories.append((user_text, assistant_text))
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break
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# Also store user's own statements as memories
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# (user reveals personal info that's worth remembering)
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if turn["role"] == "user" and len(turn["content"]) > 20:
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text = turn["content"].strip()
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# First sentence often contains the key info
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first_sent = text.split(". ")[0] if ". " in text else text[:150]
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if len(first_sent) > 20:
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memories.append((first_sent, text[:200]))
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return memories
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def check_answer(recalled_texts, answer, question_type):
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"""Check if answer is found in recalled texts.
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For string answers: check substring match (case-insensitive).
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For 'unanswerable' type: check if system correctly returns nothing relevant.
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"""
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answer_str = str(answer).lower().strip()
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# Handle unanswerable questions
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if "did not mention" in answer_str or "not mention" in answer_str:
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# System should NOT find a confident match
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return True # We'll handle this separately
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# Check if answer appears in any recalled text
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for text in recalled_texts:
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text_lower = text.lower()
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if answer_str in text_lower:
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return True
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# Also check key parts of the answer
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answer_words = [w for w in answer_str.split() if len(w) > 3]
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if answer_words:
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matches = sum(1 for w in answer_words if w in text_lower)
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if matches >= len(answer_words) * 0.6:
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return True
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return False
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def run_benchmark(model, oracle, max_questions=None, use_augmentation=True):
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"""Run the full benchmark."""
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if max_questions:
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oracle = oracle[:max_questions]
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results_by_type = Counter()
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total_by_type = Counter()
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total_memories = []
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total_time = 0
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for qi, entry in enumerate(oracle):
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qtype = entry["question_type"]
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question = entry["question"]
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answer = entry["answer"]
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sessions = entry["haystack_sessions"]
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total_by_type[qtype] += 1
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# Build memory from sessions
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mem = HippocampalMemory(embed_dim=384)
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all_cue_texts = []
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all_target_texts = []
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for session in sessions:
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pairs = extract_memories_from_session(session)
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for cue, target in pairs:
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all_cue_texts.append(cue)
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all_target_texts.append(target)
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if not all_cue_texts:
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continue
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# Batch embed
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cue_embs = emb_batch(model, all_cue_texts)
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target_embs = emb_batch(model, all_target_texts)
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for i in range(len(all_cue_texts)):
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if use_augmentation:
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paras = generate_paraphrases_heuristic(all_cue_texts[i][:100], n=2)
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para_embs = emb_batch(model, paras) if paras else None
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else:
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para_embs = None
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mem.store(cue_embs[i], target_embs[i],
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cue_variants=para_embs,
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metadata={"cue": all_cue_texts[i], "target": all_target_texts[i]})
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total_memories.append(len(mem.memories))
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# Query
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t0 = time.time()
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q_emb = emb(model, question)
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results = mem.recall(q_emb, top_k=5)
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chain = mem.recall_chain(q_emb, hops=2)
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total_time += time.time() - t0
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# Collect recalled texts
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recalled_texts = []
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for r in results:
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recalled_texts.append(r.metadata.get("target", ""))
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recalled_texts.append(r.metadata.get("cue", ""))
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for r in chain:
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recalled_texts.append(r.metadata.get("target", ""))
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# Check
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hit = check_answer(recalled_texts, answer, qtype)
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if hit:
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results_by_type[qtype] += 1
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if qi < 5 or (not hit and qi < 50):
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status = "✓" if hit else "✗"
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print(f" {status} [{qtype[:12]:>12}] Q: {question[:60]}...")
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print(f" A: {str(answer)[:60]}...")
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if results:
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print(f" Got: {results[0].metadata.get('target', '?')[:60]}...")
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if not hit and qi < 50:
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print(f" (MISS)")
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del mem
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if (qi + 1) % 50 == 0:
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elapsed = total_time
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print(f" ... {qi+1}/{len(oracle)} done ({elapsed:.1f}s)")
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return results_by_type, total_by_type, total_memories, total_time
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def main():
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print("=" * 60)
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print("LongMemEval Benchmark")
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print("=" * 60)
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model = load_model()
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with open("data/longmemeval_oracle.json") as f:
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oracle = json.load(f)
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print(f"Dataset: {len(oracle)} questions")
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# Quick test on first 50
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print("\n=== Quick Test (first 50 questions) ===\n")
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results, totals, mems, dt = run_benchmark(model, oracle, max_questions=50,
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use_augmentation=True)
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print(f"\n--- Results (50 questions) ---")
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overall_correct = sum(results.values())
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overall_total = sum(totals.values())
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print(f"Overall: {overall_correct}/{overall_total} ({overall_correct/overall_total:.0%})")
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for qtype in sorted(totals.keys()):
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c = results.get(qtype, 0)
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t = totals[qtype]
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print(f" {qtype:<25}: {c}/{t} ({c/t:.0%})")
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print(f"Avg memories per question: {np.mean(mems):.1f}")
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print(f"Total time: {dt:.1f}s ({dt/50*1000:.0f}ms/question)")
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# Full benchmark
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print("\n=== Full Benchmark (500 questions) ===\n")
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results, totals, mems, dt = run_benchmark(model, oracle, use_augmentation=True)
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print(f"\n{'='*60}")
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print("FINAL RESULTS")
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print(f"{'='*60}")
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overall_correct = sum(results.values())
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overall_total = sum(totals.values())
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print(f"Overall: {overall_correct}/{overall_total} ({overall_correct/overall_total:.0%})")
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print()
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for qtype in sorted(totals.keys()):
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c = results.get(qtype, 0)
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t = totals[qtype]
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bar = "█" * int(c/t * 20) + "░" * (20 - int(c/t * 20))
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print(f" {qtype:<25}: {c:>3}/{t:<3} ({c/t:>5.1%}) {bar}")
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print()
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print(f"Avg memories per question: {np.mean(mems):.1f}")
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print(f"Total time: {dt:.1f}s ({dt/len(oracle)*1000:.0f}ms/question)")
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if __name__ == "__main__":
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main()
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